这项荟萃分析的目的是开发和评估预测粪便中氮(N)排泄的模型,尿液,南美肉牛的粪便。该研究共纳入了1,116个粪便中N排泄的个体观察值和939个粪便和尿液中N排泄的个体观察值(g/d),代表着各种各样的饮食,动物基因型,和南美的管理条件。数据集还包括干物质摄入量(dry;kg/d)和氮摄入量(NI;g/d)的数据,膳食成分的浓度,以及平均日增重(ADG;g/d)和平均体重(BW;kg)。模型是使用线性混合效应回归得出的,该模型具有随机截距。粪便N排泄与STI呈正相关,NI,非纤维碳水化合物,平均BW,和ADG,与饮食中的EE和CP浓度呈负相关。预测粪便N排泄的单变量模型(模型1)的表现略优于单变量模型,它使用NI作为预测变量(模型2),均方根误差(RMSE)为38.0与39.2%,RMSE-观测SD比(RSR)为0.81vs.0.84,一致性相关系数(CCC)为0.53与分别为0.50。预测尿N排泄的模型不如预测粪便N排泄的模型准确,平均RMSE为43.7%37.0%,分别。尿和粪肥N排泄与STI呈正相关,NI,CP,平均BW,和ADG,并与饮食中的中性洗涤剂纤维浓度呈负相关。与粪便N排泄相反,使用NI(模型10)预测尿N排泄的单变量模型的性能略优于使用DMI(模型9)作为预测变量的单变量模型,RMSE为36.0%与39.7%,RSR0.85vs.0.93,CCC为0.43vs.分别为0.29。本研究中开发的模型适用于预测南美范围广泛的饮食组成和动物基因型中肉牛的氮排泄。建议使用使用dmi作为预测因子的单变量模型进行粪便N预测,而使用NI的单变量模型被推荐用于预测尿和粪肥N的排泄,因为使用更复杂的模型几乎没有益处。然而,当N排泄是需要考虑的因素时,考虑更复杂的模型可能更有用,这些模型包括营养摄入量和饮食组成,用于决策。本研究中评估的三个现存方程有可能在南美典型的热带条件下使用,以良好的精度和准确性预测粪便N的排泄。然而,现有的公式都不推荐用于预测尿液或粪肥N的排泄,因为它们的RMSE很高,精度和准确度低。
The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.